Object storage systems provide significant value for storing and managing data. The nature of data stored in object systems opens up opportunities to get more value out of these systems than the common expectations of cost reduction, ease of use, resilience, and durability. Maintaining the metadata for large unstructured data sets is difficult and can be time consuming. The system I propose here is an add on engine that adds Artificial Intelligence inferencing functionality to object storage systems.

The SNIA/DMTF Swordfish storage management standard provides a rich data set for managing and maintaining awareness of storage infrastructure. In this talk I'll query data from a device conforming to the Swordfish standard API and use the data for common use cases in storage management, reporting, and asset management that we have in our bespoke systems management tools. I'll look at how the data is delivered and how the various data defined by the standard are used to build the relationships we require for those common use cases.